April 20, 2018

Thematic Program on Mathematical and Quantitative Oncology
July -August 2008

Graduate courses

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Course on 'Introduction to Mathematical Oncology'

Course on Medical Image Processing

Course on Introduction to Mathematical Oncology

Instructors: M. Kohandel, S. Sivaloganathan

Description of the course:
This course will involve biology and clinical aspects of cancer, and will also present an overview of recent mathematical models developed to examine different stages of cancer growth and therapeutic strategies. It is basically designed as a course for graduate students (not for credit) who are interested
in working in the area of cancer modeling and related treatment strategies.

Lecture Times and Course Outline:

(The first hour of each lecture will cover the biology aspects, while the second half will consist of related mathematical modeling).

July 7
Lecture 1:
Introduction to cancer biology and mathematical oncology
July 11
Lecture 2:
Cancer genetics
July 18:
Lecture 3:
Tumor microenvironment
July 25,
Lecture 4:
Metastasis and invasion
August 15,
Cancer stem cells
August 22

Recommended References:
- Cancer Biology (R. Weinberg)
- The Basic Science of Oncology (Tannock et al.)
- Mathematical Biology (J. D Murray)
- Cancer Modeling and Simulation (edited by L. Preziosi)
- Computational Biology of Cancer (D. Wodarz and N. Komarova)
- Some parts of lectures will be based on review and recent articles

Course on Medical Image Processing*

Instructors: C. Drapaca, J. Trzasko, C. Studholme, H. Zhu, Y. Zinchenko

Schedule of the course:
Lectures time:
11:00 am to 1:30 pm, with 30 min for lunch from 12:00 pm to 12:30pm.
All lectures will be held at the Fields Institute.

Description of the course:
Tumors, and in particular cancer, can be difficult to detect, diagnose and treat. Digital imaging techniques, computer aided diagnosis, image-guided surgery and drug treatment can improve the accuracy and efficiency of tumors detection, diagnosis and treatment. Therefore computer assisted techniques have become a valuable component in today's clinical research and practices. This graduate course on image processing and treatment planning is a collection of feature lecture series, focusing on IMRT inverse treatment planning, CT, MR image reconstruction and various means of analyzing and processing images including image co-registration, level sets, and time-frequency analysis, etc.

August 11-12

- lectures by Dr. Hongmei Zhu (lecture slides below)
Intensity Tranforms
Spatial Transforms
Frequency Transforms
Time Frequency Transforms

August 13-14

- lectures by Dr. Corina Drapaca
August 18

- lectures by Joshua Trzasko

August 19-20
- lectures by Dr. Colin Studholme (lecture handouts)

August 21, 11 am-1:00 pm
August 22, 2:00- 4:00 pm

- lectures by Dr. Yuriy Zinchenko

August 22, 4:00-5:00 pm

-Dr. Yuriy Zinchenko Matlab Tutorial (seminar open to everybody)

The course will cover the following topics:
1. Convex and robust optimization techniques for radiotherapy treatment planning.
2. Time-frequency analysis and level set methods for imaging sciences.
3. Imaging physics, contemporary methods for fast imaging, image registration and brain image analysis.
In addition, Dr. Zinchenko's seminar on August 22 th will be a MATLAB tutorial on optimization software, (in particular, SeDuMi via MATLAB).

*This course is not for credit.

Apply to the Program:

All scientific events are open to the mathematical sciences community. Visitors who are interested in office space or funding are requested to apply by filling out the application form. Additional support is available (pending NSF funding) to support junior US visitors to this program. Fields scientific programs are devoted to research in the mathematical sciences, and enhanced graduate and post-doctoral training opportunities. Part of the mandate of the Institute is to broaden and enlarge the community, and to encourage the participation of women and members of visible minority groups in our scientific programs.

For additional information contact thematic(PUT_AT_SIGN_HERE)

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